In this paper, we study the sample complexity of one special type of neural networks: polynomial neural networks (PNNs), which uses polynomial functions as activations. We discuss both depth-dependent and depth-independent upper bound for sample complexity of PNNs, following a similar line of analysis in [1]}. There are many other interesting applications of this depth-independent bound shown in [1]}, which can be possible future directions for this paper.
